Abstract
Currently, under the conditions of permanent financial risks that hamper the sustainable economic growth in the financial sector, the development of evaluation and risk management methods both regulated by Basel II and III and others seem to be of special importance. The reputation risk is one of significant risks affecting reliability and credibility of commercial banks. The importance of reputation risk management and the quality of their assessment remain relevant as the probability of decrease in or loss of business reputation influences the financial results and the degree of customers', partners' and stakeholders' confidence. By means of imitating modeling based on Bayesian Networks and the fuzzy data analysis, the article characterizes the mechanism of reputation risk assessment and possible losses evaluation in banks by plotting normal and lognormal distribution functions. Monte-Carlo simulation is used to calculate the probability of losses caused by reputation risks. The degree of standardized histogram similarity is determined on the basis of the fuzzy data analysis applying Hamming distance method. The tree-like hierarchy based on the OWA-operator is used to aggregate the data with Fishburne's coefficients as the convolution scales. The mechanism takes into account the impact of criteria, such as return on equity, goodwill value, the risk assets ratio, the share of the productive assets in net assets, the efficiency ratio of interest bearing liabilities, the risk ratio of credit operations, the funding ratio and reliability index on the business reputation of the bank. The suggested methods and recommendations might be applied to develop the decision-making mechanism targeted at the implementation of reputation risk management system in commercial banks as well as to optimize risk management technologies.
Highlights
In the era of dynamic transformations in the financial sector of economy, tightening of requirements towards credit institutions and strengthening of competitive struggle among them make the issues of bank risk management, which include traditional forms of risks – credit, market, operational, liquidities, but reputation as well, more urgent (Dong et al, 2014; Vasylchak and Halachenko, 2016; Strielkowski et al, 2016; Masood et al, 2017)
According to standards of the international banking, every commercial bank has to develop the information security threat model which would include the description of threat sources, the vulnerabilities used by threats, methods and objects of the threat implementation, possible loss types and scales of potential damage
The sequence of the described approach is as follows: First of all, one should run a calculation of absolute probabilities of risk implementation based on the conditional probability formula allowing banks to estimate them by comparing the set of conditional probabilities and the known probabilities of risk implementation or the causes of risk implementation
Summary
In the era of dynamic transformations in the financial sector of economy, tightening of requirements towards credit institutions and strengthening of competitive struggle among them make the issues of bank risk management, which include traditional forms of risks – credit, market, operational, liquidities, but reputation as well, more urgent (Dong et al, 2014; Vasylchak and Halachenko, 2016; Strielkowski et al, 2016; Masood et al, 2017). According to standards of the international banking, every commercial bank has to develop the information security threat model which would include the description of threat sources, the vulnerabilities used by threats, methods and objects of the threat implementation, possible loss types (for example, confidentiality, integrity or assets availability) and scales of potential damage. These recommendations suggest the development of fullfledged risk model on the scenario-based analysis. The threat and violator model assess certain sources of threats (risk factors), which can cause damage to the organization with the vulnerabilities existing in this element, with each asset (Goh et al 2015; Abrham et al, 2015; Strielkowski and Höschle, 2016; Zielińska, 2016)
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